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Revert "Add tensor split support for llama.cpp (#3171)"
This reverts commit 031fe7225e
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@ -247,7 +247,6 @@ Optionally, you can use the following command-line flags:
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| `--mlock` | Force the system to keep the model in RAM. |
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| `--cache-capacity CACHE_CAPACITY` | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
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| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU. |
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| `--tensor_split TENSOR_SPLIT` | Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17 |
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| `--n_ctx N_CTX` | Size of the prompt context. |
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| `--llama_cpp_seed SEED` | Seed for llama-cpp models. Default 0 (random). |
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| `--n_gqa N_GQA` | grouped-query attention. Must be 8 for llama2 70b. |
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@ -94,12 +94,6 @@ class LlamacppHF(PreTrainedModel):
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model_file = list(path.glob('*ggml*.bin'))[0]
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logger.info(f"llama.cpp weights detected: {model_file}\n")
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if shared.args.tensor_split is None or shared.args.tensor_split.strip() == '':
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tensor_split_list = None
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else:
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tensor_split_list = [float(x) for x in shared.args.tensor_split.strip().split(",")]
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params = {
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'model_path': str(model_file),
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'n_ctx': shared.args.n_ctx,
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@ -110,7 +104,6 @@ class LlamacppHF(PreTrainedModel):
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'use_mlock': shared.args.mlock,
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'low_vram': shared.args.low_vram,
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'n_gpu_layers': shared.args.n_gpu_layers,
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'tensor_split': tensor_split_list,
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'rope_freq_base': 10000 * shared.args.alpha_value ** (64/63.),
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'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
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'n_gqa': shared.args.n_gqa or None,
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@ -41,12 +41,6 @@ class LlamaCppModel:
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cache_capacity = int(shared.args.cache_capacity)
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logger.info("Cache capacity is " + str(cache_capacity) + " bytes")
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if shared.args.tensor_split is None or shared.args.tensor_split.strip() == '':
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tensor_split_list = None
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else:
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tensor_split_list = [float(x) for x in shared.args.tensor_split.strip().split(",")]
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params = {
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'model_path': str(path),
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'n_ctx': shared.args.n_ctx,
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@ -57,7 +51,6 @@ class LlamaCppModel:
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'use_mlock': shared.args.mlock,
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'low_vram': shared.args.low_vram,
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'n_gpu_layers': shared.args.n_gpu_layers,
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'tensor_split': tensor_split_list,
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'rope_freq_base': 10000 * shared.args.alpha_value ** (64/63.),
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'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
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'n_gqa': shared.args.n_gqa or None,
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@ -33,7 +33,6 @@ loaders_and_params = {
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'n_gqa',
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'rms_norm_eps',
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'n_gpu_layers',
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'tensor_split',
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'n_batch',
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'threads',
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'no_mmap',
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@ -48,7 +47,6 @@ loaders_and_params = {
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'n_gqa',
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'rms_norm_eps',
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'n_gpu_layers',
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'tensor_split',
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'n_batch',
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'threads',
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'no_mmap',
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@ -125,7 +125,6 @@ parser.add_argument('--low-vram', action='store_true', help='Low VRAM Mode')
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parser.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.')
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parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.')
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parser.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layers to offload to the GPU.')
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parser.add_argument('--tensor_split', type=str, default=None, help="Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17")
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parser.add_argument('--n_ctx', type=int, default=2048, help='Size of the prompt context.')
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parser.add_argument('--llama_cpp_seed', type=int, default=0, help='Seed for llama-cpp models. Default 0 (random)')
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parser.add_argument('--n_gqa', type=int, default=0, help='grouped-query attention. Must be 8 for llama2 70b.')
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@ -60,7 +60,6 @@ def list_model_elements():
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'low_vram',
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'mlock',
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'n_gpu_layers',
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'tensor_split',
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'n_ctx',
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'n_gqa',
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'rms_norm_eps',
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